Multimedia Tools and Applications

, Volume 26, Issue 2, pp 221–245 | Cite as

Improving Image Retrieval Effectiveness via Multiple Queries



Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. This is the basis of the cluster hypothesis. However, semantically related images are often scattered across several visual clusters. Although traditional Content-based Image Retrieval (CBIR) technologies may utilize the information contained in multiple queries (gotten in one step or through a feedback process), this is often only a reformulation of the original query. As a result most of these strategies only get the images in some neighborhood of the original query as the retrieval result. This severely restricts the system performance. Relevance feedback techniques are generally used to mitigate this problem. In this paper, we present a novel approach to relevance feedback which can return semantically related images in different visual clusters by merging the result sets of multiple queries. We also provide experimental results to demonstrate the effectiveness of our approach.


content-based image retrieval multi-channel CBIR result merging relevance feedback 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    N. Belkin, C. Cool, W. Croft, and J. Callan, “The effect of multiple query representations on information retrieval system performance,” in Proc. of ACM SIGIR, Pittsburgh, PA, 1993, pp. 339–346.Google Scholar
  2. 2.
    N. Belkin, P. Kantor, C. Cool, and R. Quatrain, “Combining evidence for information retrieval,” in Proc. of TREC-2, Gaithersburg, MD, March 1994, pp. 35–44.Google Scholar
  3. 3.
    Y. Chen, J.Z. Wang, and R. Krovetz, “An unsupervised learning approach to content-based image retrieval,” Seventh International Symposium on Signal Processing and its Applications (ISSPA 2003), Paris, July, 2003.Google Scholar
  4. 4.
    C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, “Rank aggregation methods for the web, proceedings of the 10th international World Wide Web conference,” 2001, pp. 613–622.Google Scholar
  5. 5.
    R. Fagin, R. Kumar, and D. Sivakumar, “Comparing Top k Lists,” in Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, 2003.Google Scholar
  6. 6.
    J.C. French, A.C. Chapin, and W.N. Martin, “Multiple viewpoints as an approach to digital library interfaces,” Workshop on Document Search Interface Design and Intelligent Access in Large-scale Collections, Portland, OR, July, 2002.Google Scholar
  7. 7.
    J.C. French, X. Jin, and W.N. Martin, “An empirical investigation of the scalability of a multiple viewpoint cbir system,” International Conference on Image and Video Retrieval (CIVR 2004), Dublin, Ireland, 2004, July 21–23.Google Scholar
  8. 8.
    J.C. French, W.N. Martin, and J.V.S. Watson, “A qualitative examination of content-based image retrieval behavior using systematically modified test images,” 45th IEEE International Midwest Symposium on Circuits and Systems, Tulsa, OK, August, 2002.Google Scholar
  9. 9.
    J.C. French, J.V.S. Watson, X. Jin, and W.N. Martin, “Integrating multiple multi-channel cbir systems,” International Work Shop on Multimedia Information System (MIS’03), Ischia, Italy, May, 2003.Google Scholar
  10. 10.
    J.C. French, J.V.S. Watson, X. Jin, and W.N. Martin, “Using multiple image representations to improve the quality of content-based image retrieval,” Tech. Report CS-2003-10, Dept. of Computer Science, Univ. of Virginia, March, 2003.Google Scholar
  11. 11.
    K. Hirata and T. Kato, “Query by visual example-content based image retrieval,” in Proc. of 3rd International Conference on Extending Database Technology, Vienna, Austria, March 1992, Vol. 580 of LNCS, pp. 56–71.Google Scholar
  12. 12.
    T.S. Huang and Y. Rui, “Image retrieval: Past present and future,” in Proc. of Int. Symposium on Multimedia Information Processing, Taiwan, Dec. 1997.Google Scholar
  13. 13.
    Y. Ishikawa, R. Subramanya, and C. Faloutsos, “MindReader: Querying database through multiple examples,” 24th International Conference on Very Large Databases (VLDB’98), New York, USA, August, 1998.Google Scholar
  14. 14.
    X. Jin and J.C. French, “Improving image retrieval effectiveness via multiple queries,” in First ACM International Workshop on Multimedia Databases (MMDB’03), New Orleans, Louisiana, Nov. 2003, pp. 86–93Google Scholar
  15. 15.
    D. Kim and C. Chung “Qcluster: Relevance feedback using adaptive clustering for content-based image retrieval,” in Proc. of SIGMOD’03, San Diego, CA, June, 2003.Google Scholar
  16. 16.
    W. Liu, S. Dumais, Y. Sun, H. Zhang, M. Czerwinski, and B. Field, “Semi-automatic image annotation,” in Proc. of Human-Computer Interaction-Interact, 2001, pp. 326–333.Google Scholar
  17. 17.
    W. Niblack, R. Barber, W. Equitz, et al. “The QBIC project: Querying images by content using color, texture, and shape,” in Proc. of SPIE Electronic Imaging: Science and Technology, San Jose, CA, 1993.Google Scholar
  18. 18.
    K. Porkaew, K. Chakrabarti, and S. Mehrotra, “Query refinement for content-based multimedia retrieval in MARS,” in Proc. of the 7th. ACM Int. Conf. on Multimedia, pp. 235–238, 1999.Google Scholar
  19. 19.
    Y. Rui, T.S. Huang, M. Ortega, and S. Mehrotra, “Relevance feedback: A power tool in interactive content-based image retrieval,” IEEE Tran on Circuits and Systems for Video Technology, Special Issue on Segmentation, Description, and Retrieval of Video Content, pp. 644–655, Vol. 8, No. 5, Sept. 1998.Google Scholar
  20. 20.
    G. Sheikholeslami, W. Chang, and A. Zhang, “SemQuery: Semantic clustering and querying on heterogeneous features for visual data,” IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 14, No. 5, pp. 988–1002, September/October, 2002.Google Scholar
  21. 21.
    G. Salton, E.A. Fox, and H. Wu, “Extended boolean information retrieval,” Communications of the ACM, Vol. 26, No. 12, pp. 1022–1036, Dec. 1983.CrossRefGoogle Scholar
  22. 22.
    G. Salton and M.J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, 1989.Google Scholar
  23. 23.
    S.M.M. Tahaghoghi, J.A. Thom, and H.E. Williams, “Are two pictures better than one?” in Proc. of the 12th Australasian Database Conference (ADC’01), Gold Coast, Australia, January, 2001, pp. 138–144.Google Scholar
  24. 24.
    C.J. van Rijsbergern, Information Retrieval (2nd edition), Butterworths, London, 1979.Google Scholar
  25. 25.
    J.Z. Wang and Y. Du, “Scalable integrated region-based image retrieval using irm and statistical clustering,” in Proc. of ACM/IEEE Joint Conference on Digital Libraries (JCDL’01), Roanoke, VA, June, 2001.Google Scholar
  26. 26.
    L. Wu, C. Faloutsos, K. Sycara, and T. Payne, “Falcon: Feedback adaptive loop for content-based retrieval,” in Proc. of 26th International Conference on Very Large Data Bases (VLDB), Cairo, Egypt, 2000, pp. 297–306.Google Scholar
  27. 27.
    A. Yoshitaka and T. Ichikawa, “A survey on content-based retrieval for multimedia database,” IEEE Trans. on Knowledge and Data Engineering, Vol. 11, No. 1, pp. 81–93.Google Scholar
  28. 28.
    H. Zhang, Z. Chen, M. Li, and Z. Su, “Relevance feedback and learning in content-based image search,” World Wide Web, Vol. 6, No. 2, pp. 131–155, 2003.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of VirginiaCharlottesvilleUSA

Personalised recommendations